In advanced route planning, an optimal velocity profile of a vehicle may be computed given known disturbances along a route, for example, road grade, traffic, etc. Computation for the optimal velocity profile may be formulated into a model predictive control (MPC) problem, wherein rather than computing the entire velocity profile for the total route, the route may be broken into time horizons of a number of seconds or minutes. In the MPC problem, optimal inputs to achieve desired system outputs are computed for a given horizon. The first computed input is implemented by the vehicle system. Then, the entire time horizon is shifted forward one step, and the optimal inputs are recomputed.
One challenge in MPC is reducing the computation time. The computation time for MPC is closely related to the length of the time horizon. As the length of the time horizon increases, the computed inputs approach the optimal solution. However, a long time horizon may result in an unacceptably long computation time. For example, with a long time horizon for input optimization, solving the MPC problem in both the linear and nonlinear case may become intractable in a computational sense.
Other attempts to address the issue of long computation time in MPC include methods for increasing the optimization speed in MPC. One example approach is shown by Pekar et al. in U.S. Pat. No. 8,504,175. Therein, a cost function minimizing manipulated variables trajectories is computed with a MPC model for a relatively short time horizon in the future. The MPC uses a quadratic programming (QP) algorithm to find the optimal solution, wherein the QP algorithm is solved using an Active Sets solver (AS) class algorithm with simple constraints based on gradient projection and using a Newton step projection.
However, the inventors herein have recognized potential issues with such systems. As one example, for a given vehicle system, a long time horizon length may be necessary to obtain an adequate approximation to the optimal solution. In order to solve the MPC problem, the inputs and outputs of the model of the vehicle system are sampled. The length of the time horizon is determined by a duration of preview into the future and the sampling frequency. Higher sampling frequency leads to longer time horizon length. In order to preserve the dynamic change of the vehicle model and the model input (such as a disturbance), adequate sampling frequency is required. Thus, though a short time horizon with low sampling frequency may reduce the number of inputs to optimize, the model resolution and disturbance resolution may be lost in the process.
In one example, the issues described above may be addressed by a method of operating a vehicle responsive to a determined planned route, the planned route determined for a given time horizon to minimize fuel consumption and further based on disturbances along the planned route and according to a compressed total number of parameters of an engine torque over the given time horizon. In this way, an optimal planned route may be efficiently determined given known disturbances along the route.
As one example, during vehicle operation, vehicle parameters may be estimated online based on engine torque, one or more disturbances along the route, fuel consumption, and acceleration of the vehicle. A future engine torque may be constructed over a given time horizon, wherein the future engine torque is compressed to have a number of parameters less than the length of the time horizon. Then, each parameter of the future engine torque may be determined by minimizing a future fuel consumption. The reduced number of parameters of the future engine torque may allow faster convergence to the optimal result without sacrificing the model resolution. Further, an online torque converter modeling may be implemented to allow optimal route planning with the inclusion of discrete events requiring torque converter unlocks, such as during fuel shut off and neutral transmission operation.
The technical effect of estimating vehicle parameters online is to achieve real-time online adaptation of the vehicle model to account for changing environmental factors such as wind, vehicle mass, friction forces, aging, etc. The technical effect of compressing the number of parameters of the future engine torque is to achieve improved optimization efficiency without sacrificing model resolution and disturbance resolution. Moreover, the compression may significantly reduce the required computational resources and enable on-board implementation of the algorithm.
It should be understood that the summary above is provided to introduce in simplified form a selection of concepts that are further described in the detailed description. It is not meant to identify key or essential features of the claimed subject matter, the scope of which is defined uniquely by the claims that follow the detailed description. Furthermore, the claimed subject matter is not limited to implementations that solve any disadvantages noted above or in any part of this disclosure.